{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据特征分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1、分布分析<br>\n",
    "2、对比分析<br>\n",
    "3、周期分析<br>\n",
    "4、贡献度分析<br>\n",
    "5、相关性分析<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.rc('font', **{'family' : 'HiraginoSansGB-W3, PingFangSC-Regular, Microsoft YaHei, SimHe'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分布分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1、定量数据的分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4624.34536366, 2388.24358635, 2471.82824774, 1927.03137784,\n",
       "       3865.40762932, 4744.81176422, 2238.7930991 , 3319.03909606,\n",
       "       2750.62962452, 4462.10793704, 2677.58279599, 2615.94564533,\n",
       "       4133.76944234, 1900.10873269, 2827.57179245, 2122.14158208,\n",
       "       3042.21374672, 3582.81521372, 1899.38082279, 4144.72370984,\n",
       "       3901.59072059, 3502.4943389 , 3900.85594926, 2316.27214083,\n",
       "       2877.10977448, 2064.23056574, 2732.11192037, 3530.35546674,\n",
       "       2308.33924827, 2603.24647314, 2312.82729988, 2154.7943585 ,\n",
       "       2328.75386916, 2987.33540108, 1882.68965136, 3234.41569782,\n",
       "       4659.80217711, 3742.04416058, 2808.16444764, 2112.37103592,\n",
       "       2252.84170625, 4692.45460103, 3050.80775478, 2363.00435343,\n",
       "       3190.91548467, 5100.25513648, 3120.15895248, 3617.20310971,\n",
       "       3300.17031996, 2647.75015351, 1857.48180198, 2650.65727759,\n",
       "       2791.10576663, 3586.62319118, 3838.98341387, 3931.1020813 ,\n",
       "       3285.58732525, 3885.14116427, 2245.602059  , 4252.86815523,\n",
       "       3512.92982042, 2701.9071649 , 3488.51814654, 2924.42828698,\n",
       "       4131.62938745, 4519.81681642, 5185.57540653, 2495.53413705,\n",
       "       3160.03706945, 3876.16892112, 3315.63494724, 2693.79598737,\n",
       "       3827.97464261, 3230.09473536, 3762.01118031, 2777.67185739,\n",
       "       2799.24193107, 3186.56139099, 3410.05164721, 3198.29972013,\n",
       "       3119.00864581, 2329.33771371, 3377.56378632, 3121.82127099,\n",
       "       4129.48390791, 4198.9178799 , 3185.15641748, 2624.71504991,\n",
       "       2361.26959255, 3423.49435406, 3077.34006835, 2656.14632443,\n",
       "       3043.59685683, 2379.99915605, 3698.03203407])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "data = np.random.normal(3000, 1000, size=100)\n",
    "data = data[data>1800]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3328.0936045553017"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range_val = np.max(data) - np.min(data)      # 极差\n",
    "range_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "bins = math.ceil(range_val/400)\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 9., 14., 18., 19., 11., 10.,  6.,  6.,  2.]),\n",
       " array([1857.48180198, 2227.26998026, 2597.05815855, 2966.84633683,\n",
       "        3336.63451511, 3706.4226934 , 4076.21087168, 4445.99904997,\n",
       "        4815.78722825, 5185.57540653]),\n",
       " <a list of 9 Patch objects>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data, bins=bins, rwidth=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = plt.hist(data, bins=bins, rwidth=0.8, range=(1800, 400*bins+1800))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9., 14., 19., 19., 12., 13.,  3.,  4.,  2.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1800., 2200., 2600., 3000., 3400., 3800., 4200., 4600., 5000.,\n",
       "       5400.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2、定性数据的分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [114, 456, 295]\n",
    "labels = ['snacks','fruit','daily-necessities']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(labels, data, width=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.axes(aspect=1)\n",
    "plt.pie(data, labels=labels, autopct='%.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对比分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>部门A</th>\n",
       "      <th>部门B</th>\n",
       "      <th>部门C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>115</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110</td>\n",
       "      <td>120</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108</td>\n",
       "      <td>121</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>123</td>\n",
       "      <td>105</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>119</td>\n",
       "      <td>115</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>103</td>\n",
       "      <td>116</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>123</td>\n",
       "      <td>119</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>95</td>\n",
       "      <td>180</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>98</td>\n",
       "      <td>176</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>92</td>\n",
       "      <td>178</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>90</td>\n",
       "      <td>179</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100</td>\n",
       "      <td>170</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    部门A  部门B  部门C\n",
       "1   101  115  200\n",
       "2   110  120  200\n",
       "3   108  121  190\n",
       "4   123  105  201\n",
       "5   119  115  199\n",
       "6   103  116  202\n",
       "7   123  119  200\n",
       "8    95  180  152\n",
       "9    98  176  197\n",
       "10   92  178  189\n",
       "11   90  179  201\n",
       "12  100  170  180"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    "    '部门A': [101, 110, 108, 123, 119, 103, 123, 95, 98, 92, 90, 100],\n",
    "    '部门B': [115, 120, 121, 105, 115, 116, 119, 180, 176, 178, 179, 170],\n",
    "    '部门C': [200, 200, 190, 201, 199, 202, 200, 152, 197, 189, 201, 180]\n",
    "}\n",
    "df = pd.DataFrame(data, index=np.arange(1, 13))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = df.plot()\n",
    "plt.savefig('对比分析.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119e40ba8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 周期性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      0\n",
      "2018-08-13 00:00:00  24\n",
      "2018-08-13 01:00:00  23\n",
      "2018-08-13 02:00:00  22\n",
      "2018-08-13 03:00:00  21\n",
      "2018-08-13 04:00:00  20\n",
      "                      0\n",
      "2018-08-15 19:00:00  28\n",
      "2018-08-15 20:00:00  27\n",
      "2018-08-15 21:00:00  25\n",
      "2018-08-15 22:00:00  24\n",
      "2018-08-15 23:00:00  24\n"
     ]
    }
   ],
   "source": [
    "# 模拟三天的气温\n",
    "y = [24, 23, 22, 21, 20, 20, 20, 21, 24, 26, 28, 29, 30, 31, 32, 32, 32, 32, 31, 28, 27, 25, 24, 24]*3\n",
    "x = pd.date_range('2018-08-13', periods = 72, freq = 'H')\n",
    "df = pd.DataFrame(y, index=x)\n",
    "print(df.head())\n",
    "print(df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 贡献度分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>服装</th>\n",
       "      <td>1888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>手机</th>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>家电</th>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>玩具</th>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>零食</th>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>汽配</th>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>图书</th>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>办公</th>\n",
       "      <td>888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机票</th>\n",
       "      <td>503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电脑</th>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    profit\n",
       "服装    1888\n",
       "手机    1999\n",
       "家电    2000\n",
       "玩具     334\n",
       "零食     113\n",
       "汽配    1770\n",
       "图书     124\n",
       "办公     888\n",
       "机票     503\n",
       "电脑     333"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data = {\n",
    "#     'profit': [1888, 1999, 2000, 334, 113, 1770, 124, 888, 503, 333]\n",
    "# }\n",
    "data = [1888, 1999, 2000, 334, 113, 1770, 124, 888, 503, 333]\n",
    "index = ['服装', '手机', '家电', '玩具', '零食', '汽配', '图书', '办公', '机票', '电脑']\n",
    "\n",
    "df = pd.DataFrame(data, index=index, columns=['profit'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>服装</th>\n",
       "      <td>1888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>手机</th>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>家电</th>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>玩具</th>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>零食</th>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>汽配</th>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>图书</th>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>办公</th>\n",
       "      <td>888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机票</th>\n",
       "      <td>503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电脑</th>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    profit\n",
       "服装    1888\n",
       "手机    1999\n",
       "家电    2000\n",
       "玩具     334\n",
       "零食     113\n",
       "汽配    1770\n",
       "图书     124\n",
       "办公     888\n",
       "机票     503\n",
       "电脑     333"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = {\n",
    "    'profit': [1888, 1999, 2000, 334, 113, 1770, 124, 888, 503, 333]\n",
    "}\n",
    "index2 = ['服装', '手机', '家电', '玩具', '零食', '汽配', '图书', '办公', '机票', '电脑']\n",
    "\n",
    "df0 = pd.DataFrame(data2, index=index2)\n",
    "df0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.sort_values('profit', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>家电</th>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>手机</th>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服装</th>\n",
       "      <td>1888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>汽配</th>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>办公</th>\n",
       "      <td>888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机票</th>\n",
       "      <td>503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>玩具</th>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电脑</th>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>图书</th>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>零食</th>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    profit\n",
       "家电    2000\n",
       "手机    1999\n",
       "服装    1888\n",
       "汽配    1770\n",
       "办公     888\n",
       "机票     503\n",
       "玩具     334\n",
       "电脑     333\n",
       "图书     124\n",
       "零食     113"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>家电</th>\n",
       "      <td>0.200965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>手机</th>\n",
       "      <td>0.401829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服装</th>\n",
       "      <td>0.591539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>汽配</th>\n",
       "      <td>0.769393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>办公</th>\n",
       "      <td>0.858621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机票</th>\n",
       "      <td>0.909164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>玩具</th>\n",
       "      <td>0.942725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电脑</th>\n",
       "      <td>0.976186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>图书</th>\n",
       "      <td>0.988645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>零食</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      profit\n",
       "家电  0.200965\n",
       "手机  0.401829\n",
       "服装  0.591539\n",
       "汽配  0.769393\n",
       "办公  0.858621\n",
       "机票  0.909164\n",
       "玩具  0.942725\n",
       "电脑  0.976186\n",
       "图书  0.988645\n",
       "零食  1.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = df2.cumsum()/df2.sum()\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.plot(style='c--o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 相关性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>delivery-time</th>\n",
       "      <th>minimum-delivery-amount</th>\n",
       "      <th>sales-volume</th>\n",
       "      <th>consumption-per-person</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>200</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>400</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>400</td>\n",
       "      <td>120</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>500</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>600</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>600</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>700</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>25</td>\n",
       "      <td>50</td>\n",
       "      <td>800</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>900</td>\n",
       "      <td>60</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1000</td>\n",
       "      <td>58</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>1000</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    delivery-time  minimum-delivery-amount  sales-volume  \\\n",
       "0              12                       15           100   \n",
       "1              15                       18           200   \n",
       "2              15                       18           400   \n",
       "3              18                       20           400   \n",
       "4              18                       20           500   \n",
       "5              20                       30           600   \n",
       "6              20                       30           600   \n",
       "7              25                       50           700   \n",
       "8              25                       50           800   \n",
       "9              10                       10           900   \n",
       "10             10                       10          1000   \n",
       "11             12                       15          1000   \n",
       "\n",
       "    consumption-per-person  grade  \n",
       "0                      100      1  \n",
       "1                       50      1  \n",
       "2                       80      1  \n",
       "3                      120      2  \n",
       "4                       60      2  \n",
       "5                       30      3  \n",
       "6                      200      3  \n",
       "7                       90      3  \n",
       "8                       40      4  \n",
       "9                       60      4  \n",
       "10                      58      5  \n",
       "11                      20      5  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    "    'delivery-time': [12, 15, 15, 18, 18, 20, 20, 25, 25, 10, 10, 12],\n",
    "    'minimum-delivery-amount': [15, 18, 18, 20, 20, 30, 30, 50, 50, 10, 10, 15],\n",
    "    'sales-volume': [100, 200, 400, 400, 500, 600, 600, 700, 800, 900, 1000, 1000],\n",
    "    'consumption-per-person': [100, 50, 80, 120, 60, 30, 200, 90, 40, 60, 58, 20],\n",
    "    'grade': [1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 5]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### corr() 查看相关性系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>delivery-time</th>\n",
       "      <th>minimum-delivery-amount</th>\n",
       "      <th>sales-volume</th>\n",
       "      <th>consumption-per-person</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>delivery-time</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.949072</td>\n",
       "      <td>-0.064532</td>\n",
       "      <td>0.187174</td>\n",
       "      <td>-0.090585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minimum-delivery-amount</th>\n",
       "      <td>0.949072</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.082768</td>\n",
       "      <td>0.092060</td>\n",
       "      <td>0.070583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales-volume</th>\n",
       "      <td>-0.064532</td>\n",
       "      <td>0.082768</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.314258</td>\n",
       "      <td>0.965059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>consumption-per-person</th>\n",
       "      <td>0.187174</td>\n",
       "      <td>0.092060</td>\n",
       "      <td>-0.314258</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.295704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>-0.090585</td>\n",
       "      <td>0.070583</td>\n",
       "      <td>0.965059</td>\n",
       "      <td>-0.295704</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         delivery-time  minimum-delivery-amount  sales-volume  \\\n",
       "delivery-time                 1.000000                 0.949072     -0.064532   \n",
       "minimum-delivery-amount       0.949072                 1.000000      0.082768   \n",
       "sales-volume                 -0.064532                 0.082768      1.000000   \n",
       "consumption-per-person        0.187174                 0.092060     -0.314258   \n",
       "grade                        -0.090585                 0.070583      0.965059   \n",
       "\n",
       "                         consumption-per-person     grade  \n",
       "delivery-time                          0.187174 -0.090585  \n",
       "minimum-delivery-amount                0.092060  0.070583  \n",
       "sales-volume                          -0.314258  0.965059  \n",
       "consumption-per-person                 1.000000 -0.295704  \n",
       "grade                                 -0.295704  1.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.corr?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr, cmap=sns.color_palette('Blues'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False, False],\n",
       "       [False, False, False, False, False],\n",
       "       [False, False, False, False, False],\n",
       "       [False, False, False, False, False],\n",
       "       [False, False, False, False, False]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 4]),\n",
       " array([0, 1, 2, 3, 4, 1, 2, 3, 4, 2, 3, 4, 3, 4, 4]))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind = np.triu_indices_from(mask)\n",
    "ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask[ind] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True,  True,  True],\n",
       "       [False,  True,  True,  True,  True],\n",
       "       [False, False,  True,  True,  True],\n",
       "       [False, False, False,  True,  True],\n",
       "       [False, False, False, False,  True]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr, cmap=sns.color_palette('Blues'), mask=mask)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))      # 设置图片大小\n",
    "sns.heatmap(corr, cmap=sns.color_palette('Blues'), mask=mask)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
